Asynchronous techniques for system-on-chip design

SoC design will require asynchronous techniques as the large parameter variations across the chip will make it impossible to control delays in clock networks and other global signals efficiently. Initially, SoCs will be globally asynchronous and locally synchronous (GALS). But the complexity of the numerous asynchronous/synchronous interfaces required in a GALS will eventually lead to entirely asynchronous solutions. This paper introduces the main design principles, methods, and building blocks for asynchronous VLSI systems, with an emphasis on communication and synchronization. Asynchronous circuits with the only delay assumption of isochronic forks are called quasi-delay-insensitive (QDI). QDI is used in the paper as the basis for asynchronous logic. The paper discusses asynchronous handshake protocols for communication and the notion of validity/neutrality tests, and completion tree. Basic building blocks for sequencing, storage, function evaluation, and buses are described, and two alternative methods for the implementation of an arbitrary computation are explained. Issues of arbitration, and synchronization play an important role in complex distributed systems and especially in GALS. The two main asynchronous/synchronous interfaces needed in GALS-one based on synchronizer, the other on stoppable clock-are described and analyzed

A Static Analyzer for Large Safety-Critical Software

We show that abstract interpretation-based static program analysis can be made efficient and precise enough to formally verify a class of properties for a family of large programs with few or no false alarms. This is achieved by refinement of a general purpose static analyzer and later adaptation to particular programs of the family by the end-user through parametrization. This is applied to the proof of soundness of data manipulation operations at the machine level for periodic synchronous safety critical embedded software. The main novelties are the design principle of static analyzers by refinement and adaptation through parametrization, the symbolic manipulation of expressions to improve the precision of abstract transfer functions, the octagon, ellipsoid, and decision tree abstract domains, all with sound handling of rounding errors in floating point computations, widening strategies (with thresholds, delayed) and the automatic determination of the parameters (parametrized packing)

Models for robust resource allocation in project scheduling.

The vast majority of resource-constrained project scheduling efforts assumes complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. In reality, however, project activities are subject to considerable uncertainty which generally leads to numerous schedule disruptions. In this paper, we present a resource allocation model that protects the makespan of a given baseline schedule against activity duration variability. A branch-and-bound algorithm is developed that solves the proposed robust resource allocation problem in exact and approximate formulations. The procedure relies on constraint propagation during its search. We report on computational results obtained on a set of benchmark problems.Model; Resource allocation; Scheduling;

Human-in-the-Loop Model Predictive Control of an Irrigation Canal

Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].Junta de Andalucía P11-TEP-812

A formal analysis of complexity and systemic risk in financial networks with derivatives

The 2008 financial crisis has been attributed by policymakers to “excessive complexity” of the financial network, especially due to financial derivatives. In a financial network, financial institutions (“banks” for short) are connected by financial contracts. As banks depend on payments from contracts with other banks to cover their own obligations, such a situation creates systemic risk, i.e., the risk of a financial crisis. Some of the contracts are financial derivatives, where an obligation to pay depends on another variable. In this thesis, I study in what sense derivatives make a financial network fundamentally “more complex” compared to one without derivatives. I capture the notion of “complexity” formally using tools from finance and theoretical computer science. I reveal new kinds of systemic risk that arise in financial networks specifically because of derivatives and I discuss the impact of recent regulatory policy. I first focus on a type of derivative called a credit default swap (CDS), in which the writer insures the holder of the contract against the default (i.e., bankruptcy) of a third party, the reference entity. I show that, when the reference entity is another bank, then such CDSs introduce a new kind of systemic risk arising from what I call default ambiguity. Default ambiguity is a situation where it is impossible to decide which banks are in default following a shock (i.e., a loss in banks’ assets). At a technical level, I show that the clearing problem may have no solution or multiple incompatible solutions. In contrast, without CDSs, a unique canonical solution always exists. I then demonstrate that increased “complexity” due to CDSs also manifests as computational complexity. More in detail, I show that the clearing problem leads to NP-complete decision and PPAD-complete approximation problems if CDSs are allowed. This implies a fundamental barrier to the computational analysis of these networks, specifically to macroprudential stress testing. Without CDSs, the problems are either trivial or in P. I study the impact of different regulatory policies. My main result is that the aforementioned phenomena can be attributed to naked CDS positions. In a final step, I focus on one specific regulatory policy: mandatory portfolio compression, which is a post-trade mechanism by which cycles in the financial network are eliminated. While this always reduces individual exposures, I show that, surprisingly, it can worsen the impact of certain shocks. Banks’ incentives to compress may further be misaligned with social welfare. I provide sufficient conditions on the network structure under which these issues are eliminated. Overall, my results in this thesis contribute to a better understanding of systemic risk and the effects of regulatory policy

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

This article has been made available through the Brunel Open Access Publishing Fund. Copyright @ 2013 Pey et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. Results: We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. Conclusions: A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.Basque Governmen